Defocus blur is a widely used camera lens effect in computer generated imagery. in movies and games today. Offline renderers typically simulate defocus by sampling numerous positions on the lens using distribution ray tracing, stochastic rasterization or multi-layer rendering. Real-time renderers approximate the effect with samples from a traditional two dimensional (2D) rasterizer, resulting in objectionable visual artifacts but fast performance. Although significant research progress has been made in the area of real-time stochastic rendering and ray tracing, the large number of samples required to produce noise free defocus blur images remains a key challenge in making these higher-quality rendering approaches practical for real-time.
Recent advancements in light field reconstruction techniques have made it possible to reproduce low-noise defocus blur images with a small number of samples; however, the computational overhead of the reconstruction algorithms preclude interactive performance. Reconstruction techniques based on frequency analysis and sheared filtering are particularly promising. These techniques suppress noise by using local derivatives of the light field to derive sheared reconstruction filters that tightly bound the frequency spectrum of the light field. Unfortunately, these filters result in noisy reconstruction when the local derivatives vary significantly over a pixel, such as when in-focus and out-of-focus objects contribute to the same pixel. Moreover, variations in the light field derivatives can lead to different filtering parameters for each pixel. This prevents efficient separable filter implementations and greatly impacts performance.